Decoding quantum field theory with machine learning

DOI10.1007/jhep08(2023)031arXiv1910.03637OpenAlexW4385706383MaRDI QIDQ6050327

José Polo-Gómez, Eduardo Martín-Martínez, Irene Melgarejo-Lermas, Daniel Grimmer

Publication date: 12 October 2023

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.03637

zbMATH Keywords

nonperturbative effectslattice quantum field theory

Mathematics Subject Classification ID

Quantum theory (81-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• More ado about nothing
• Quantum information capsule and information delocalization by entanglement in multiple-qubit systems
• A bound on chaos
• A classification of open Gaussian dynamics
• How often does the Unruh–DeWitt detector click? Regularization by a spatial profile
• Then again, how often does the Unruh–DeWitt detector click if we switch it carefully?
• Information in black hole radiation
• Robust online Hamiltonian learning
• Application of a neural network to the sign problem via the path optimization method
• Continuous Variable Quantum Information: Gaussian States and Beyond
• Quantum information processing and relativistic quantum fields
• Looking inside a black hole

This page was built for publication: Decoding quantum field theory with machine learning